Overview

Dataset statistics

Number of variables11
Number of observations7760
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory727.5 KiB
Average record size in memory96.0 B

Variable types

Text1
DateTime1
Numeric9

Alerts

PER is highly overall correlated with PBVHigh correlation
PBV is highly overall correlated with PERHigh correlation
ATR is highly overall correlated with CCC and 1 other fieldsHigh correlation
CCC is highly overall correlated with ATRHigh correlation
ROA is highly overall correlated with ATR and 1 other fieldsHigh correlation
DER is highly overall correlated with EMHigh correlation
NPM is highly overall correlated with ROAHigh correlation
EM is highly overall correlated with DERHigh correlation
PER is highly skewed (γ1 = 22.7492753)Skewed
NPM is highly skewed (γ1 = 72.8588197)Skewed

Reproduction

Analysis started2023-09-25 05:01:39.981739
Analysis finished2023-09-25 05:02:04.290431
Duration24.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Stock
Text

Distinct388
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size121.2 KiB
2023-09-24T23:02:04.381588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.1572165
Min length1

Characters and Unicode

Total characters24500
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA
ValueCountFrequency (%)
a 20
 
0.3%
avy 20
 
0.3%
aap 20
 
0.3%
aapl 20
 
0.3%
abbv 20
 
0.3%
abc 20
 
0.3%
abt 20
 
0.3%
acgl 20
 
0.3%
acn 20
 
0.3%
adbe 20
 
0.3%
Other values (378) 7560
97.4%
2023-09-24T23:02:04.538889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 2120
 
8.7%
C 2040
 
8.3%
M 1620
 
6.6%
P 1440
 
5.9%
E 1380
 
5.6%
L 1320
 
5.4%
N 1300
 
5.3%
T 1120
 
4.6%
D 1120
 
4.6%
S 1080
 
4.4%
Other values (17) 9960
40.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 24460
99.8%
Dash Punctuation 40
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 2120
 
8.7%
C 2040
 
8.3%
M 1620
 
6.6%
P 1440
 
5.9%
E 1380
 
5.6%
L 1320
 
5.4%
N 1300
 
5.3%
T 1120
 
4.6%
D 1120
 
4.6%
S 1080
 
4.4%
Other values (16) 9920
40.6%
Dash Punctuation
ValueCountFrequency (%)
- 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24460
99.8%
Common 40
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 2120
 
8.7%
C 2040
 
8.3%
M 1620
 
6.6%
P 1440
 
5.9%
E 1380
 
5.6%
L 1320
 
5.4%
N 1300
 
5.3%
T 1120
 
4.6%
D 1120
 
4.6%
S 1080
 
4.4%
Other values (16) 9920
40.6%
Common
ValueCountFrequency (%)
- 40
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 2120
 
8.7%
C 2040
 
8.3%
M 1620
 
6.6%
P 1440
 
5.9%
E 1380
 
5.6%
L 1320
 
5.4%
N 1300
 
5.3%
T 1120
 
4.6%
D 1120
 
4.6%
S 1080
 
4.4%
Other values (17) 9960
40.7%

fiscalDateEnding
Date

Distinct20
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size121.2 KiB
Minimum2018-12-31 00:00:00
Maximum2023-09-30 00:00:00
2023-09-24T23:02:04.594912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:04.631142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)

PER
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct7716
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean228.07845
Minimum-123728.5
Maximum161045
Zeros18
Zeros (%)0.2%
Negative732
Negative (%)9.4%
Memory size121.2 KiB
2023-09-24T23:02:04.676595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-123728.5
5-th percentile-58.722822
Q118.129977
median62.617803
Q3177.50682
95-th percentile926.80826
Maximum161045
Range284773.5
Interquartile range (IQR)159.37684

Descriptive statistics

Standard deviation3434.4083
Coefficient of variation (CV)15.058013
Kurtosis1489.0689
Mean228.07845
Median Absolute Deviation (MAD)56.022125
Skewness22.749275
Sum1769888.8
Variance11795160
MonotonicityNot monotonic
2023-09-24T23:02:04.718269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18
 
0.2%
-70.4775189 15
 
0.2%
-75.47612986 7
 
0.1%
-71.00819419 3
 
< 0.1%
-72.27952706 3
 
< 0.1%
-71.20157667 2
 
< 0.1%
-75.47612986 2
 
< 0.1%
26.71540371 2
 
< 0.1%
8.300000191 1
 
< 0.1%
7.362496344 1
 
< 0.1%
Other values (7706) 7706
99.3%
ValueCountFrequency (%)
-123728.4964 1
< 0.1%
-40759.19907 1
< 0.1%
-28491.46586 1
< 0.1%
-19976.73812 1
< 0.1%
-17172.82938 1
< 0.1%
-16700.97198 1
< 0.1%
-13977.9039 1
< 0.1%
-13006.17501 1
< 0.1%
-12314.40628 1
< 0.1%
-12054.38721 1
< 0.1%
ValueCountFrequency (%)
161045.0024 1
< 0.1%
159068.1563 1
< 0.1%
75509.49379 1
< 0.1%
66125.99882 1
< 0.1%
49336.28849 1
< 0.1%
47009.95449 1
< 0.1%
36520.21266 1
< 0.1%
24474.89026 1
< 0.1%
18436.51721 1
< 0.1%
15024.14112 1
< 0.1%

PBV
Real number (ℝ)

HIGH CORRELATION 

Distinct7699
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4103238
Minimum0
Maximum173.5946
Zeros55
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size121.2 KiB
2023-09-24T23:02:04.760831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.52076823
Q11.4476282
median2.6007535
Q34.839791
95-th percentile11.542671
Maximum173.5946
Range173.5946
Interquartile range (IQR)3.3921628

Descriptive statistics

Standard deviation8.6102063
Coefficient of variation (CV)1.9522844
Kurtosis148.91692
Mean4.4103238
Median Absolute Deviation (MAD)1.4446105
Skewness10.564266
Sum34224.113
Variance74.135652
MonotonicityNot monotonic
2023-09-24T23:02:04.803697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 55
 
0.7%
1.513719011 2
 
< 0.1%
1.580964829 2
 
< 0.1%
2.154762686 2
 
< 0.1%
1.134720098 2
 
< 0.1%
3.270621178 2
 
< 0.1%
0.7918548114 2
 
< 0.1%
1.770351288 2
 
< 0.1%
0.4556253032 1
 
< 0.1%
3.702375081 1
 
< 0.1%
Other values (7689) 7689
99.1%
ValueCountFrequency (%)
0 55
0.7%
0.04144543299 1
 
< 0.1%
0.06177724214 1
 
< 0.1%
0.071838083 1
 
< 0.1%
0.1298783275 1
 
< 0.1%
0.1395668616 1
 
< 0.1%
0.1462115399 1
 
< 0.1%
0.1720946131 1
 
< 0.1%
0.1754023987 1
 
< 0.1%
0.1779691524 1
 
< 0.1%
ValueCountFrequency (%)
173.5946011 1
< 0.1%
166.6958221 1
< 0.1%
163.6570327 1
< 0.1%
151.0548789 1
< 0.1%
148.525295 1
< 0.1%
144.9230468 1
< 0.1%
138.4621355 1
< 0.1%
133.609105 1
< 0.1%
132.1688787 1
< 0.1%
129.7744752 1
< 0.1%

Acid_test
Real number (ℝ)

Distinct1000
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.069926
Minimum3.2237017
Maximum76.111111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size121.2 KiB
2023-09-24T23:02:04.848963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.2237017
5-th percentile3.2964468
Q110.50128
median20.887855
Q335.933333
95-th percentile63.156806
Maximum76.111111
Range72.887409
Interquartile range (IQR)25.432054

Descriptive statistics

Standard deviation19.922175
Coefficient of variation (CV)0.7946643
Kurtosis0.33145174
Mean25.069926
Median Absolute Deviation (MAD)12.199957
Skewness1.0663723
Sum194542.63
Variance396.89307
MonotonicityNot monotonic
2023-09-24T23:02:04.891987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.97016529 671
 
8.6%
53.78181818 388
 
5.0%
13.28292683 388
 
5.0%
4.074675325 388
 
5.0%
3.300275482 388
 
5.0%
3.223701731 388
 
5.0%
62.475 388
 
5.0%
35.93333333 388
 
5.0%
8.687898089 388
 
5.0%
21.64615385 388
 
5.0%
Other values (990) 3597
46.4%
ValueCountFrequency (%)
3.223701731 388
5.0%
3.300275482 388
5.0%
3.455155451 1
 
< 0.1%
3.564091168 1
 
< 0.1%
3.564091168 1
 
< 0.1%
3.579405919 2
 
< 0.1%
3.579405919 4
 
0.1%
3.734285887 6
 
0.1%
3.734285887 93
 
1.2%
4.074675325 388
5.0%
ValueCountFrequency (%)
76.11111111 388
5.0%
62.475 388
5.0%
60.88247475 1
 
< 0.1%
60.65494398 1
 
< 0.1%
57.92772176 1
 
< 0.1%
56.41661616 1
 
< 0.1%
56.09449954 1
 
< 0.1%
55.4280303 1
 
< 0.1%
54.44480184 1
 
< 0.1%
53.78181818 388
5.0%

ATR
Real number (ℝ)

HIGH CORRELATION 

Distinct7616
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.72037196
Minimum-0.01047065
Maximum13.58879
Zeros41
Zeros (%)0.5%
Negative1
Negative (%)< 0.1%
Memory size121.2 KiB
2023-09-24T23:02:04.936768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.01047065
5-th percentile0.041321296
Q10.13050149
median0.29180489
Q30.62751521
95-th percentile3.2522944
Maximum13.58879
Range13.599261
Interquartile range (IQR)0.49701372

Descriptive statistics

Standard deviation1.2915601
Coefficient of variation (CV)1.7929073
Kurtosis20.726502
Mean0.72037196
Median Absolute Deviation (MAD)0.19438403
Skewness4.0439625
Sum5590.0864
Variance1.6681275
MonotonicityNot monotonic
2023-09-24T23:02:04.983993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 41
 
0.5%
0.3217599235 2
 
< 0.1%
4.353146483 2
 
< 0.1%
7.276072431 2
 
< 0.1%
6.065203176 2
 
< 0.1%
5.898389095 2
 
< 0.1%
5.319676861 2
 
< 0.1%
5.800909655 2
 
< 0.1%
4.791731588 2
 
< 0.1%
4.011837419 2
 
< 0.1%
Other values (7606) 7701
99.2%
ValueCountFrequency (%)
-0.01047065045 1
 
< 0.1%
0 41
0.5%
3.351690087 × 10-51
 
< 0.1%
7.765011119 × 10-51
 
< 0.1%
8.205879659 × 10-51
 
< 0.1%
0.0002981342566 1
 
< 0.1%
0.0004163568773 1
 
< 0.1%
0.0006770541186 1
 
< 0.1%
0.000704902577 1
 
< 0.1%
0.0008461131676 1
 
< 0.1%
ValueCountFrequency (%)
13.58879018 1
< 0.1%
13.25680256 1
< 0.1%
12.97860244 1
< 0.1%
12.510726 1
< 0.1%
11.99971515 1
< 0.1%
11.93483103 1
< 0.1%
11.73137509 1
< 0.1%
11.49685235 1
< 0.1%
11.45348356 1
< 0.1%
11.0938307 1
< 0.1%

CCC
Real number (ℝ)

HIGH CORRELATION 

Distinct7672
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1948036 × 108
Minimum-147204.29
Maximum3.0649265 × 1010
Zeros0
Zeros (%)0.0%
Negative36
Negative (%)0.5%
Memory size121.2 KiB
2023-09-24T23:02:05.030598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-147204.29
5-th percentile18.005136
Q1114.74466
median273.12462
Q3872.06146
95-th percentile12561.375
Maximum3.0649265 × 1010
Range3.0649412 × 1010
Interquartile range (IQR)757.3168

Descriptive statistics

Standard deviation1.8218451 × 109
Coefficient of variation (CV)5.7025263
Kurtosis37.535944
Mean3.1948036 × 108
Median Absolute Deviation (MAD)221.49103
Skewness5.873751
Sum2.4791676 × 1012
Variance3.3191197 × 1018
MonotonicityNot monotonic
2023-09-24T23:02:05.074738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.046691239 × 101011
 
0.1%
1.046691239 × 10105
 
0.1%
1.046691236 × 10105
 
0.1%
1.046691233 × 10104
 
0.1%
1.046691234 × 10103
 
< 0.1%
15.24348163 2
 
< 0.1%
10.32351732 2
 
< 0.1%
12.03395511 2
 
< 0.1%
12.2127481 2
 
< 0.1%
12.66634322 2
 
< 0.1%
Other values (7662) 7722
99.5%
ValueCountFrequency (%)
-147204.2878 1
< 0.1%
-137523.8329 1
< 0.1%
-130209.0496 1
< 0.1%
-83510.73572 1
< 0.1%
-26103.59382 1
< 0.1%
-25728.98745 1
< 0.1%
-21593.08639 1
< 0.1%
-13222.47328 1
< 0.1%
-8696.492674 1
< 0.1%
-6852.504474 1
< 0.1%
ValueCountFrequency (%)
3.064926475 × 10101
< 0.1%
2.168529415 × 10101
< 0.1%
1.046693733 × 10101
< 0.1%
1.046693728 × 10101
< 0.1%
1.046693693 × 10101
< 0.1%
1.046692727 × 10101
< 0.1%
1.046692084 × 10101
< 0.1%
1.046691856 × 10101
< 0.1%
1.046691684 × 10101
< 0.1%
1.04669151 × 10101
< 0.1%

ROA
Real number (ℝ)

HIGH CORRELATION 

Distinct7448
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.078678907
Minimum-5.2613432
Maximum3.8390627
Zeros38
Zeros (%)0.5%
Negative703
Negative (%)9.1%
Memory size121.2 KiB
2023-09-24T23:02:05.118910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-5.2613432
5-th percentile-0.020419328
Q10.011799508
median0.028678188
Q30.074035265
95-th percentile0.31842123
Maximum3.8390627
Range9.1004059
Interquartile range (IQR)0.062235757

Descriptive statistics

Standard deviation0.25693705
Coefficient of variation (CV)3.2656408
Kurtosis85.974038
Mean0.078678907
Median Absolute Deviation (MAD)0.022046016
Skewness2.8195327
Sum610.54832
Variance0.066016646
MonotonicityNot monotonic
2023-09-24T23:02:05.161073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38
 
0.5%
0.003797949107 4
 
0.1%
0.01640103781 3
 
< 0.1%
0.001108401685 3
 
< 0.1%
0.01180182286 3
 
< 0.1%
0.03622152321 3
 
< 0.1%
0.02441816101 3
 
< 0.1%
0.02742287317 3
 
< 0.1%
0.0131147541 3
 
< 0.1%
0.006030469742 3
 
< 0.1%
Other values (7438) 7694
99.1%
ValueCountFrequency (%)
-5.261343205 1
< 0.1%
-4.160721099 1
< 0.1%
-2.836461126 1
< 0.1%
-2.660551531 1
< 0.1%
-2.15376866 1
< 0.1%
-2.074633037 1
< 0.1%
-1.590438685 1
< 0.1%
-1.43508373 1
< 0.1%
-1.403932082 1
< 0.1%
-1.081649921 1
< 0.1%
ValueCountFrequency (%)
3.839062651 1
< 0.1%
3.704258838 1
< 0.1%
3.36412178 1
< 0.1%
3.353345599 1
< 0.1%
3.289844329 1
< 0.1%
3.130466397 1
< 0.1%
3.069045364 1
< 0.1%
2.972400248 1
< 0.1%
2.754556882 1
< 0.1%
2.747321183 1
< 0.1%

DER
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.95748051
Minimum0.76039024
Maximum1.1617464
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size121.2 KiB
2023-09-24T23:02:05.197470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.76039024
5-th percentile0.77674072
Q10.90406324
median0.97929573
Q31.0061995
95-th percentile1.1231399
Maximum1.1617464
Range0.40135612
Interquartile range (IQR)0.10213624

Descriptive statistics

Standard deviation0.10155725
Coefficient of variation (CV)0.10606717
Kurtosis-0.30446066
Mean0.95748051
Median Absolute Deviation (MAD)0.060269967
Skewness-0.14559141
Sum7430.0488
Variance0.010313874
MonotonicityNot monotonic
2023-09-24T23:02:05.234302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0.9206549118 388
 
5.0%
0.866805051 388
 
5.0%
0.7776012708 388
 
5.0%
0.7603902439 388
 
5.0%
0.8169370129 388
 
5.0%
0.9907329402 388
 
5.0%
0.9597772277 388
 
5.0%
0.983011745 388
 
5.0%
0.916482634 388
 
5.0%
0.975579725 388
 
5.0%
Other values (10) 3880
50.0%
ValueCountFrequency (%)
0.7603902439 388
5.0%
0.7776012708 388
5.0%
0.8169370129 388
5.0%
0.8623993033 388
5.0%
0.866805051 388
5.0%
0.916482634 388
5.0%
0.9206549118 388
5.0%
0.9466928151 388
5.0%
0.9597772277 388
5.0%
0.975579725 388
5.0%
ValueCountFrequency (%)
1.161746362 388
5.0%
1.121107966 388
5.0%
1.059320369 388
5.0%
1.041194846 388
5.0%
1.013738551 388
5.0%
1.003686457 388
5.0%
0.9907329402 388
5.0%
0.9864538875 388
5.0%
0.9852968897 388
5.0%
0.983011745 388
5.0%

NPM
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct7661
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.78454264
Minimum-441.99742
Maximum4156.7819
Zeros1
Zeros (%)< 0.1%
Negative707
Negative (%)9.1%
Memory size121.2 KiB
2023-09-24T23:02:05.278372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-441.99742
5-th percentile-0.076904444
Q10.05808925
median0.11937179
Q30.19992203
95-th percentile0.36950849
Maximum4156.7819
Range4598.7793
Interquartile range (IQR)0.14183278

Descriptive statistics

Standard deviation51.065146
Coefficient of variation (CV)65.089064
Kurtosis5751.3324
Mean0.78454264
Median Absolute Deviation (MAD)0.068268353
Skewness72.85882
Sum6088.0509
Variance2607.6491
MonotonicityNot monotonic
2023-09-24T23:02:05.319765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1576390587 11
 
0.1%
1 7
 
0.1%
0.0780027367 5
 
0.1%
0.05208866276 4
 
0.1%
0.01696296747 3
 
< 0.1%
-0.03796353821 3
 
< 0.1%
0.0226744186 2
 
< 0.1%
-0.09700854701 2
 
< 0.1%
0.1831916123 2
 
< 0.1%
0.2278236073 2
 
< 0.1%
Other values (7651) 7719
99.5%
ValueCountFrequency (%)
-441.9974194 1
< 0.1%
-164.3289835 1
< 0.1%
-106.2926141 1
< 0.1%
-103.9223688 1
< 0.1%
-92.19354839 1
< 0.1%
-77.14093329 1
< 0.1%
-75.88461538 1
< 0.1%
-67.33333333 1
< 0.1%
-42.28571429 1
< 0.1%
-15.5 1
< 0.1%
ValueCountFrequency (%)
4156.78187 1
< 0.1%
1475.941964 1
< 0.1%
711.8544153 1
< 0.1%
20.71794872 1
< 0.1%
12.38383838 1
< 0.1%
7.94 1
< 0.1%
7.407407407 1
< 0.1%
7.074676142 1
< 0.1%
5.274333528 1
< 0.1%
4.603559871 1
< 0.1%

EM
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9575241
Minimum1.7603902
Maximum2.1617464
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size121.2 KiB
2023-09-24T23:02:05.356552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.7603902
5-th percentile1.7767407
Q11.9040632
median1.9792957
Q32.0061995
95-th percentile2.1231399
Maximum2.1617464
Range0.40135612
Interquartile range (IQR)0.10213624

Descriptive statistics

Standard deviation0.10151664
Coefficient of variation (CV)0.051859717
Kurtosis-0.30128596
Mean1.9575241
Median Absolute Deviation (MAD)0.060269967
Skewness-0.14593436
Sum15190.387
Variance0.010305629
MonotonicityNot monotonic
2023-09-24T23:02:05.392486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1.920654912 388
 
5.0%
1.866805051 388
 
5.0%
1.777601271 388
 
5.0%
1.760390244 388
 
5.0%
1.816937013 388
 
5.0%
1.99073294 388
 
5.0%
1.959777228 388
 
5.0%
1.983011745 388
 
5.0%
1.916482634 388
 
5.0%
1.975579725 388
 
5.0%
Other values (10) 3880
50.0%
ValueCountFrequency (%)
1.760390244 388
5.0%
1.777601271 388
5.0%
1.816937013 388
5.0%
1.863270194 388
5.0%
1.866805051 388
5.0%
1.916482634 388
5.0%
1.920654912 388
5.0%
1.946692815 388
5.0%
1.959777228 388
5.0%
1.975579725 388
5.0%
ValueCountFrequency (%)
2.161746362 388
5.0%
2.121107966 388
5.0%
2.059320369 388
5.0%
2.041194846 388
5.0%
2.013738551 388
5.0%
2.003686457 388
5.0%
1.99073294 388
5.0%
1.986453888 388
5.0%
1.98529689 388
5.0%
1.983011745 388
5.0%

Interactions

2023-09-24T23:02:03.819177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:00.838040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:01.324559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:01.668311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:02.013062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:02.351671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:02.720569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:03.041362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:03.395826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:03.857365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:00.903672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:01.361651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:01.704958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:02.048990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:02.394681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:02.754762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:03.079823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:03.430182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:03.896539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:01.011141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:01.399161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:01.744433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:02.087711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:02.439462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:02.790612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:03.119556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:03.567298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:03.936788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:01.083130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:01.438451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:01.782656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:02.125822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:02.484028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:02.827510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:03.160143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:03.604047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:03.976499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:01.137219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:01.477058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:01.822109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:02.163843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:02.530099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:02.863459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:03.200276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:03.640627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:04.016429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:01.174765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:01.515891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:01.860249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:02.201714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:02.568216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:02.899925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:03.239468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:03.676508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:04.052420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:01.209957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:01.550410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:01.895139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:02.236633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:02.602854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:02.931880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:03.275196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:03.709702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:04.095070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:01.250185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:01.592107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:01.937021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:02.276918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:02.645397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:02.971045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:03.317122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:03.748215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:04.130975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:01.284785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:01.626845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:01.971631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:02.311664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:02.679729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:03.003146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:03.353291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-24T23:02:03.780800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-09-24T23:02:05.429689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
PERPBVAcid_testATRCCCROADERNPMEM
PER1.0000.520-0.007-0.4820.357-0.3230.0010.0740.001
PBV0.5201.0000.019-0.001-0.0170.1630.0590.2160.059
Acid_test-0.0070.0191.0000.0110.0080.026-0.4050.011-0.405
ATR-0.482-0.0010.0111.000-0.8130.6330.009-0.1870.009
CCC0.357-0.0170.008-0.8131.000-0.4790.0290.2120.029
ROA-0.3230.1630.0260.633-0.4791.0000.0420.5250.042
DER0.0010.059-0.4050.0090.0290.0421.0000.0561.000
NPM0.0740.2160.011-0.1870.2120.5250.0561.0000.056
EM0.0010.059-0.4050.0090.0290.0421.0000.0561.000

Missing values

2023-09-24T23:02:04.188743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-24T23:02:04.251731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

StockfiscalDateEndingPERPBVAcid_testATRCCCROADERNPMEM
0A2023-09-30294.0822623.05790553.7818180.154379519.7380420.0103980.9206550.0673541.920655
1A2023-06-30117.4627483.28704126.9701650.156505589.9751040.0279840.8668050.1788041.866805
2A2023-03-31115.9207863.73698312.4663870.159538570.1892550.0322370.9466930.2020671.946693
3A2022-12-31119.5405944.17688476.1111110.174041496.8228630.0349410.9852970.2007641.985297
4A2022-09-30108.6218313.40867814.8888890.163201498.7071910.0313811.0593200.1922852.059320
5A2022-06-30128.5065093.36784215.4628570.151698497.0274330.0262081.0411950.1727622.041195
6A2022-03-31138.8703743.8055895.9199640.160066459.0873010.0274041.0036860.1712042.003686
7A2021-12-31107.8445934.4528089.9679010.154507457.6193600.0412890.9864540.2672311.986454
8A2021-09-30178.5280004.49255521.6461540.149175461.5851430.0251641.1211080.1686902.121108
9A2021-06-30204.4686034.24747213.2829270.144739450.4318000.0207731.1617460.1435222.161746
StockfiscalDateEndingPERPBVAcid_testATRCCCROADERNPMEM
10XRAY2021-03-31180.4027741.9580628.6878980.111588623.2866320.0108541.0137390.0972672.013739
11XRAY2020-12-31331.2431281.61716335.9333330.091441763.4979860.0048820.9755800.0533911.975580
12XRAY2020-09-30-137.9427771.37278162.4750000.0501051210.568532-0.0099520.916483-0.1986201.916483
13XRAY2020-06-30-97.9845341.3886763.2237020.091740747.716001-0.0141720.983012-0.1544851.983012
14XRAY2020-03-31113.5811081.2229603.3002750.116988591.9903400.0107670.9597770.0920381.959777
15XRAY2019-12-31198.0109671.7806744.0746750.101090645.9638110.0089930.9907330.0889591.990733
16XRAY2019-09-30436.2336911.84103326.9701650.116104579.9502720.0042200.8169370.0363491.816937
17XRAY2019-06-30452.6074861.96655026.9701650.104921596.1905850.0043450.7603900.0414111.760390
18XRAY2019-03-318389.1529081.68682726.9701650.118376517.7731440.0002010.7776010.0016991.777601
19XRAY2018-12-31409.0248151.33824426.9701650.107350232.4121160.0032720.8623990.0304781.863270